Paper Info
Title
Expectation maximization approach to vessel enhancemet in thoracic CT scans
Abstract
Vessel enhancement in volumetric data is a necessary prerequisite ill various medical imaging applications. In the context of automated lung nodule detection in thoracic CT scans, segmented blood vessels call be used to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. Segmenting, the data correctly is a difficult problem with direct consequences for subsequent processing steps. Voxels belonging to vessels and nodules in thoracic CT scans are both characterized by high contrast with respect to a local neighborhood. Thus in order to enhance vessels while suppressing nodules, additional characteristics should be used. In this paper we propose a novel vessel enhancement filter that is capable of enhancing vessels and junctions in thoracic CT seal-is while suppressing nodules. The proposed filters are based oil a Gaussian mixture model which is optimized through expectation maximization. The proposed filters are based on first order differential quantities and so are less sensitive to noise compared with known Hessian-based vessel enhancement filters. Moreover, the proposed filters utilize an adaptive window and so avoid the common need for multiple scale analysis. The proposed filters are evaluated and compared to known techniques qualitatively and quantitatively on both synthetic and actual clinical data and it is shown that the proposed filters perform better.
Year
DOI
Venue
2005
10.1117/12.595453
PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
Keywords
Field
DocType
medical imaging,computer-aided diagnosis,vessel enhancement,vessel segmentation,nodule detection,probabilistic models
Voxel,Computer vision,First order,Computer science,Medical imaging,Expectation–maximization algorithm,Hessian matrix,Artificial intelligence,Volumetric data,Mixture model
Conference
Volume
ISSN
Citations 
5747
0277-786X
1
PageRank 
References 
Authors
0.37
8
2
Name
Order
Citations
PageRank
Gady Agam139143.99
Changhua Wu218916.89